Abstract
Detecting abnormal users in social networks is crucial for protecting user privacy and preventing criminal activities. However, existing graph learning methods have limitations. Unsupervised methods focus on topological anomalies and may overlook user characteristics, while supervised methods require costly data annotations. To address these challenges, we propose a weakly supervised framework called Anomaly Detection Graph Convolutional Network (ADGCN). Our model includes three modules: information-preserving compression of user features, collaborative mining of global and local graph information, and multi-view weakly supervised classification. We demonstrate that ADGCN generates high-quality user representations using minimal labeled data and achieves state-of-the-art performance on two real-world social network datasets. Ablation experiments and performance analyses show the feasibility and effectiveness of our approach in practical scenarios.
Z. Shen, T. Zhang and H. He—Contributed equally to this research.
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Notes
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The source code is available at https://github.com/zxlearningdeep/ADGCN-project.
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References
Bo, D., Wang, X., Shi, C., Shen, H.: Beyond low-frequency information in graph convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 3950–3957 (2021)
Cai, S., et al.: Rethinking graph neural architecture search from message-passing. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6657–6666 (2021)
Clevert, D.A., Unterthiner, T., Hochreiter, S.: Fast and accurate deep network learning by exponential linear units (ELUS). arXiv preprint arXiv:1511.07289 (2015)
Gilmer, J., Schoenholz, S.S., Riley, P.F., Vinyals, O., Dahl, G.E.: Neural message passing for quantum chemistry. In: International Conference on Machine Learning, pp. 1263–1272. PMLR (2017)
Hamilton, W., Ying, Z., Leskovec, J.: Inductive representation learning on large graphs. In: Advances in Neural Information Processing Systems 30 (2017)
Hu, T., Qi, H., Huang, Q., Lu, Y.: See better before looking closer: weakly supervised data augmentation network for fine-grained visual classification. arXiv preprint arXiv:1901.09891 (2019)
Jiang, J., et al.: Anomaly detection with graph convolutional networks for insider threat and fraud detection. In: MILCOM 2019–2019 IEEE Military Communications Conference (MILCOM), pp. 109–114. IEEE (2019)
Khan, W., Haroon, M.: An efficient framework for anomaly detection in attributed social networks. Int. J. Inf. Technol. 14(6), 3069–3076 (2022)
Khan, W., Haroon, M.: An unsupervised deep learning ensemble model for anomaly detection in static attributed social networks. Int. J. Cognit. Comput. Eng. 3, 153–160 (2022)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)
Kipf, T.N., Welling, M.: Variational graph auto-encoders. arXiv preprint arXiv:1611.07308 (2016)
Lee, D.H., et al.: Pseudo-label: the simple and efficient semi-supervised learning method for deep neural networks. In: Workshop on Challenges in Representation Learning, ICML, vol. 3, p. 896 (2013)
Li, Y., Huang, X., Li, J., Du, M., Zou, N.: Specae: spectral autoencoder for anomaly detection in attributed networks. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, pp. 2233–2236 (2019)
Liu, F., Tian, Y., Chen, Y., Liu, Y., Belagiannis, V., Carneiro, G.: ACPL: anti-curriculum pseudo-labelling for semi-supervised medical image classification. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 20697–20706 (2022)
Nt, H., Maehara, T.: Revisiting graph neural networks: all we have is low-pass filters. arXiv preprint arXiv:1905.09550 (2019)
Pennebaker, J.W., Francis, M.E., Booth, R.J.: Linguistic Inquiry and Word Count: LIWC 2001, vol. 71. Lawrence Erlbaum Associates, Mahway (2001)
Savage, D., Zhang, X., Yu, X., Chou, P., Wang, Q.: Anomaly detection in online social networks. Soc. Netw. 39, 62–70 (2014)
Tosyali, A., Kim, J., Choi, J., Kang, Y., Jeong, M.K.: New node anomaly detection algorithm based on nonnegative matrix factorization for directed citation networks. Ann. Oper. Res. 288, 457–474 (2020)
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y.: Graph attention networks. arXiv preprint arXiv:1710.10903 (2017)
Vignac, C., Loukas, A., Frossard, P.: Building powerful and equivariant graphneural networks with structural message-passing. In: Advances in Neural Information Processing Systems, vol. 33, pp. 14143–14155 (2020)
Wu, F., Souza, A., Zhang, T., Fifty, C., Yu, T., Weinberger, K.: Simplifying graph convolutional networks. In: International Conference on Machine Learning, pp. 6861–6871. PMLR (2019)
You, Y., Chen, T., Sui, Y., Chen, T., Wang, Z., Shen, Y.: Graph contrastivelearning with augmentations. In: Advances in Neural Information Processing Systems, vol. 33, pp. 5812–5823 (2020)
Yu, R., Qiu, H., Wen, Z., Lin, C., Liu, Y.: A survey on social media anomaly detection. ACM SIGKDD Explor. Newsl 18(1), 1–14 (2016)
Zhu, Y., Xu, Y., Yu, F., Liu, Q., Wu, S., Wang, L.: Graph contrastive learning with adaptive augmentation. In: Proceedings of the Web Conference 2021, pp. 2069–2080 (2021)
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Shen, Z., Zhang, T., He, H. (2024). ADGCN: A Weakly Supervised Framework for Anomaly Detection in Social Networks. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Communications in Computer and Information Science, vol 1965. Springer, Singapore. https://doi.org/10.1007/978-981-99-8145-8_20
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